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PubMed2026

Cross-tissue Mendelian randomization prioritizes RAB27B as a brain-derived candidate protein for postpartum depression.

OBJECTIVE: Postpartum depression (PPD) is one of the most common and debilitating complications of childbirth, yet the candidate proteins linking genetic risk to disease remain poorly defined. Building on recent genome-wide association studies (GWAS), we sought to integrate cross-tissue proteogenomic data to identify candidate proteins for PPD and explore therapeutic opportunities. METHODS: We conducted two-sample Mendelian randomization (MR) using genome-wide significant cis-protein QTLs from brain (n = 608 proteins), cerebrospinal fluid (CSF; n = 214), and plasma (n = 612). PPD summary statistics were obtained from FinnGen R8 (13,657 cases, 236,178 controls) and replicated in an independent GWAS. Phenome-wide association (PheWAS) was used to assess pleiotropy. Potential therapeutic targets were evaluated through DSigDB drug repurposing, molecular docking, and molecular dynamics simulations. RESULTS: Among all proteins tested, RAB27B was the only brain-derived protein surpassing Bonferroni correction (OR = 1.60; 95% CI: 1.30-1.96; P = 6.6 × 10⁻⁶), whereas no significant proteins were identified in CSF or plasma. This association was replicated in an independent GWAS (OR = 1.27; 95% CI: 1.02-1.58; P = 0.037). PheWAS identified no pleiotropic associations at genome-wide significance. In silico drug repurposing identified pregnenolone as a candidate ligand with computationally predicted stable binding to RAB27B, providing a starting point for future experimental validation. CONCLUSION: This study provides the first cross-tissue proteogenomic evidence that RAB27B is a brain-derived, reproducible candidate protein genetically associated with PPD. By extending GWAS signals to functional protein-level mechanisms and therapeutic inference, our findings nominate RAB27B and pregnenolone as promising directions for postpartum psychiatric research.

Humans↗
PubMed2025

Integrating multi-omics approaches in acute myeloid leukemia (AML): Advancements and clinical implications.

Acute myeloid leukemia (AML) is a highly heterogeneous and aggressive hematologic malignancy characterized by clonal proliferation of myeloid precursors. Despite significant advancements in genomic profiling and targeted therapies, patient outcomes remain suboptimal due to disease complexity, resistance mechanisms, and high relapse rates. The integration of multi-omics approaches-spanning genomics, epigenomics, transcriptomics, proteomics, and metabolomics-has revolutionized AML research, offering a comprehensive understanding of leukemogenesis, tumor heterogeneity, and therapeutic vulnerabilities. Recent studies leveraging high-throughput sequencing, mass spectrometry, and advanced computational tools have uncovered novel biomarkers, clonal evolution dynamics, and microenvironmental interactions that drive AML progression and resistance. For instance, single-cell multi-omics has revealed chemotherapy-resistant leukemic stem cell populations, while proteogenomic analyses have identified actionable targets such as MCL1 and metabolic dependencies like OXPHOS. Clinically, integrated omics platforms are refining risk stratification, minimal residual disease (MRD) monitoring, and personalized therapy selection. However, challenges such as data integration complexity, cost barriers, and ethical considerations remain. This review highlights the transformative potential of multi-omics in AML, emphasizing recent advancements in technology, biomarker discovery, and therapeutic innovation. By bridging the gap between molecular insights and clinical practice, multi-omics integration promises to redefine AML management, paving the way for precision oncology and improved patient outcomes.

Humans↗
PubMed2026

The mighty microproteins: from versatile cellular regulators to precision medicine therapeutics.

Microproteins, are tiny proteins encoded by small open reading frame (sORF), translation of these non-canonical open reading frames (ncORFs) has been implicated in diverse biological processes and diseases. This review summarizes recent developments in the discovery, biogenesis, and functional characterization of microproteins, and their involvement in various disease, with special focus on their roles in cancer, cardiovascular, metabolic, neurodegenerative and immune-related disorders. We emphasize the regulation of key cellular pathways by microproteins, including mitochondrial homeostasis, apoptosis, metabolic reprogramming, and immune signaling, all of which affect disease initiation and progression. Emerging evidence also supports their potential as disease biomarkers and therapeutic candidates for precision medicine. Finally, the review critically discusses the current challenges including discrepancies in microprotein annotation, the limitations of ribosome profiling and proteogenomic approaches, the gap between computationally predicted and experimentally validated microproteins, and the need for rigorous orthogonal validation by means of CRISPR-based genome editing, ribosome release assays, mutational analysis, high-resolution mass spectrometry, and functional studies. Finally, we review recent development of AI-assisted ORF prediction, single-cell translatomics, spatial proteomics, and integrated multi-omics as emerging technologies reshaping. Microprotein discovery and functional annotation. Finally, we discuss the translational potential of microproteins and highlight the remaining challenges to clinical application, including peptide stability, pharmacokinetics, tissue-specific delivery, immunogenicity, and the need for rigorous preclinical and clinical validation. Together, this review provides an updated and critical overview of the rapidly evolving microprotein field and highlights future research priorities for translating these molecules into clinically useful biomarkers and precision therapeutics.

Microproteins↗
PubMed2026

Pancreatic ductal adenocarcinoma: The Vision of Heracles.

Pancreatic ductal adenocarcinoma (PDAC) remains among the deadliest malignancies, as tumors evolve faster than therapies. Resistance is ecological, not merely KRAS driven, involving overlooked players like high-grade pancreatic intraepithelial neoplasias (PanINs), peripancreatic fat, stromal mechanics, myeloid-neural circuits, metabolic rewiring, and systemic host responses. We propose precision interception targeting PanIN/intraductal papillary mucinous neoplasm (IPMN) biology, spatial-functional-proteogenomic classification beyond transcriptomics, the Heracles Protocol (measure, prime, strike, and adapt), and integrated technologies from AI pathology to exosomal delivery and CRISPR-based synergy mapping, together making PDAC more tractable.

Humans↗
PubMed2025

Immune landscape and novel therapeutic targets of epidermal growth factor receptor and anaplastic lymphoma kinase wild type never-smoker lung adenocarcinoma.

BACKGROUND: Never-smoker lung adenocarcinoma (NSLA) exhibits distinct immunosuppressive profiles and a lower tumor mutation burden compared with lung adenocarcinoma in smokers. These correlate with poor responses to immune checkpoint inhibitors. In this study, we aimed to elucidate the tumor-immune microenvironment of NSLA without epidermal growth factor receptor (EGFR) or anaplastic lymphoma kinase (ALK) alterations and identify novel therapeutic targets. METHODS: We analyzed genome, transcriptome, and proteomic data from 102 NSLA tumor samples and 16 normal adjacent tissues. We classified tumors into distinct immune clusters (IC) based on gene signatures by profiling the tumor-infiltrating immune cells. RESULTS: The tumors were stratified into three ICs: hot, intermediate, and cold. Notably, only 21 (20.6%) patients exhibited hot IC enriched in cytotoxic T cells, natural killer cells, and B-cell signatures, which correlated with improved recurrence-free survival. Cold ICs (37.3%) exhibited higher myeloid-derived suppressor cell (MDSC) levels and M2 macrophage signatures, with poor immune cell infiltration and relatively low stimulatory cytokines and chemokines expression. CEACAM1, and NECTIN2 were upregulated in intermediate and cold ICs and correlated with MDSC and M2 macrophage infiltration. High expression of these genes was associated with poor survival outcomes. Protein-protein network analysis of 20 upregulated molecules associated with cancer- and driver-related proteins in cold IC identified XPO 1 as a key component. CONCLUSION: Our proteogenomic analysis highlighted the immunosuppressive properties of NSLA without EGFR and ALK alterations and identified novel therapeutic targets. These findings may provide novel treatment strategies that could improve the clinical outcomes of patients with NSLA.

Humans↗
PubMed2024

ST3GAL1 Promotes Malignant Phenotypes in Intrahepatic Cholangiocarcinoma.

Intrahepatic cholangiocarcinoma (iCCA) has a poor prognosis, and elucidation of the molecular mechanisms underlying iCCA malignancy is of great significance. Glycosylation, an important post-translational modification, is closely associated with tumor progression. Altered glycosylation, including aberrant sialylation resulting from abnormal expression of sialyltransferases (STs) and neuraminidases (NEUs), is a significant feature of cancer cells. However, there is limited information on the roles of STs and NEUs in iCCA malignancy. Here, utilizing our proteogenomic resources from a cohort of 262 patients with iCCA, we identified ST3GAL1 as a prognostically relevant molecule in iCCA. Moreover, overexpression of ST3GAL1 promoted proliferation, migration, and invasion and inhibited apoptosis of iCCA cells in vitro. Through proteomic analyses, we identified the downstream pathway potentially regulated by ST3GAL1, which was the NF-κB signaling pathway, and further demonstrated that this pathway was positively correlated with malignancy in iCCA cells. Notably, glycoproteomics showed that O-glycosylation was changed in iCCA cells with high ST3GAL1 expression. Importantly, the altered O-glycopeptides underscored the potential utility of O-glycosylation profiling as a discriminatory marker for iCCA cells with ST3GAL1 overexpression. Additionally, miR-320b was identified as a post-transcriptional regulator of ST3GAL1, capable of suppressing ST3GAL1 expression and then reducing the proliferation, migration, and invasion abilities of iCCA cell lines. Taken together, these results suggest ST3GAL1 could serve as a promising therapeutic target for iCCA.

Female↗
PubMed2026

Noncanonical Transcription and Splicing Shape the Colorectal Cancer Immunopeptidome in MSI and MSS Tumors.

Treatment with immune checkpoint inhibitors in colorectal cancer (CRC) has largely benefited patients with microsatellite instability-high (MSI-H) and not the larger proportion of patient with microsatellite-stable (MSS) tumors. This clinical dichotomy has fueled the view that high mutational burden is the dominant driver of tumor immunogenicity and that MSS CRC fails to respond because it is "antigen poor". To directly test this premise and define the origins of presented tumor antigens, we integrated HLA class I immunopeptidomics and matched RNA-seq from 26 primary CRC tumors spanning MSI-H and MSS subtypes. Using patient-specific canonical and cancer-specific proteogenomic databases, we identified 115,292 unique major histocompatibility complex (MHC)-associated peptides (MAPs) across 61 HLA alleles, with a mean of 9292 MAPs per tumor and no significant difference in MAP counts between MSI-H and MSS tumors. In toto, we identified 266 tumor antigens, all coded by unmutated genomic sequences, comprising 70 aberrantly expressed tumor-specific antigens (aeTSAs) and 196 tumor-associated antigens (TAAs). In our cohort, MSS tumors presented more TAAs and a comparable number of aeTSAs per tumor relative to MSI-H tumors. In TCGA-COAD stratified analyses (483 tumors), MSS tumors yielded more presentable aeTSAs and TAAs per patient than MSI-H tumors. Across both subtypes, aeTSAs arose predominantly from intronic translation, UTR usage, retroelement activation, and germline-like transcription, including recurrent aeTSAs from PIWIL1, L1TD1, and endogenous retroviral loci. Together, these data demonstrate that MSS CRC is not antigen poor and highlight noncanonical translation as a major, previously underappreciated contributor to the CRC immunopeptidome.

Humans↗
PubMed2002

Capillary array high-performance liquid chromatography of nucleic acids and proteins.

An array of monolithic poly(styrene/divinylbenzene) capillaries with individual column thermostats was constructed to demonstrate its utility for the separation of nucleic acids, proteins, and tryptic digests in combination with UV absorbance detection. Because of polymerization-related variation in surface area of monolithic columns, the concentration of acetonitrile required for elution of DNA fragments in denaturing HPLC may vary sufficiently to affect the degree of denaturation. Modulation of column temperature offers a convenient way to harmonize elution profiles among columns. Individual regulation of column temperature also provides the means to determine rapidly in a single parallel run the optimum temperature for resolution of biomolecules. Given the high reproducibility of separations among columns and the ease with which poly(styrene/divinylbenzene)-based stationary phases can be modified to accommodate different modes of chromatography, such arrays will find broad applicability in proteogenomics.

Chromatography, High Pressure Liquid↗
PubMed2026

Mass Spectrometry-Based Profiling of Personalized Immunopeptidomes in Thai Renal Cell Carcinoma.

This study profiles the personalized immunopeptidomes of 13 Thai patients with renal cell carcinoma (RCC), addressing a critical knowledge gap in Southeast Asian populations characterized by distinct HLA allele distributions. We combined whole-exome sequencing (WES)-based personalized proteome construction with liquid chromatography-tandem mass spectrometry (LC-MS/MS), using both database-driven searches and de novo peptide sequencing. HLA typing identified several class I allotypes that are underrepresented in publicly available immunopeptidome resources, including seven alleles not previously represented in the databases examined; HLA-A*11:01 was the most frequent allele in this cohort. Database-based analysis identified a single tumor-specific neoantigen derived from a mutant JADE2 peptide in the patient with the highest tumor mutational burden, which was validated by a mutant-specific ELISPOT response. In contrast, de novo sequencing revealed numerous noncanonical peptides, a subset of which were supported by proteogenomic validation using PepQuery and detected exclusively in cancer proteomes but not in normal tissue data sets, indicating their potential as tumor-associated antigen candidates. Together, these results establish an integrated and scalable framework for identifying HLA-presented tumor-derived peptides and provide a foundational immunopeptidome resource to support personalized cancer immunotherapy development in Southeast Asia.

Humans↗
PubMed2026

Foundation model enables interpretable open and error-tolerant searching for mass spectrometry-based proteomics.

MOTIVATION: Mass spectrometry-based proteomics allows studying all proteins of a sample on a molecular level. However, mass spectra are noisy and contain complex patterns, making them inherently challenging to analyze with algorithmic approaches. In terms of the protein sequence landscape, most recent bottom-up MS-based proteomics studies consider either a diverse pool of post-translational modifications, employ large databases-as in metaproteomics or proteogenomics, study multiple isoforms of proteins, include unspecific cleavage sites or even combinations thereof. All this makes peptide and protein identifications challenging. RESULTS: Here, we present a foundation model, called yHydra, that jointly embeds spectra and peptides. This allows us to implement various downstream tasks and search modes in Euclidean space. We implement an open search which allows querying multiple ten-thousands of spectra against millions of peptides. Furthermore, we implement an error-tolerant search for identifying additional proteoforms that are not included in off-the-shelf reference proteomes. Our foundation model provides meaningful embeddings, as we interpret learned peptide embeddings in comparison to the peptide's physico-chemical properties. Hydra's open search, assigns delta masses to each identification which allows to unrestrictedly characterize post-translational modifications. The error-tolerant mode of yHydra can be used as post-processing to existing search engines or as a standalone. yHydra is evaluated on several real life data sets for the identification of modified peptide sequences and shows up to 25% increase in peptide identification at constant false discovery rate compared to the current state-of-the-art. AVAILABILITY AND IMPLEMENTATION: Code is available on Gitlab: https://gitlab.com/dacs-hpi/yHydra, and https://gitlab.com/dacs-hpi/yHydra_train.

Proteomics↗
PubMed2026

Network methods for diagonal integration of unpaired single-cell multiomics data: a review.

MOTIVATION: Advances in single-cell sequencing have enabled multiomics profiling at unprecedented resolution; however, mass spectrometry-based single-cell proteomics (scMS) remains inherently destructive, precluding simultaneous transcriptomic capture. Unlike antibody-based methods such as CITE-seq, which permit paired profiling but are restricted to targeted protein panels, scMS provides unbiased, genome-scale coverage of the intracellular proteome yet necessitates post hoc integration of unpaired datasets. This diagonal integration challenge, where transcriptomes and proteomes are measured in separate cells lacking shared anchors, remains underserved by existing reviews, which focus predominantly on vertical integration strategies enabled by non-destructive assays. RESULTS: We survey the complete computational pipeline for constructing mechanistic proteogenomic networks from unpaired single-cell data, covering: (i) unimodal network inference such as knowledge-based approaches, probabilistic graphical models, temporal directionality inference, and generative and foundation model strategies that establish the transcriptomic scaffold; (ii) cross-modal integration architectures such as network propagation, graph neural networks (scMRDR, scmFormer, scCotag), and consensus frameworks designed explicitly for the unpaired proteomics setting; and (iii) benchmarking paradigms spanning network reconstruction (BEELINE, GRETA, CausalBench) and multi-task integration evaluation (scMultiBench, SCMMIB), with guidance on metric selection under network sparsity and class imbalance. We identify three principal axes of future development: generative proteomic translation from transcriptomic precursors, inductive prior embedding in next-generation architectures, and perturbation-based causal benchmarking. AVAILABILITY AND IMPLEMENTATION: This is a review article; no novel software is distributed. A curated benchmark resource table, methods starter guide, and per-method bottleneck annotations are provided in the Supplementary Material.

Multiomics↗
PubMed2002

Putting the Leishmania genome to work: functional genomics by transposon trapping and expression profiling.

Leishmania are important protozoan pathogens of humans in temperate and tropical regions. The study of gene expression during the infectious cycle, in mutants or after environmental or chemical stimuli, is a powerful approach towards understanding parasite virulence and the development of control measures. Like other trypanosomatids, Leishmania gene expression is mediated by a polycistronic transcriptional process that places increased emphasis on post-transcriptional regulatory mechanisms including RNA processing and protein translation. With the impending completion of the Leishmania genome, global approaches surveying mRNA and protein expression are now feasible. Our laboratory has developed the Drosophila transposon mariner as a tool for trapping Leishmania genes and studying their regulation in the form of protein fusions; a classic approach in other microbes that can be termed 'proteogenomics'. Similarly, we have developed reagents and approaches for the creation of DNA microarrays, which permit the measurement of RNA abundance across the parasite genome. Progress in these areas promises to greatly increase our understanding of global mechanisms of gene regulation at both mRNA and protein levels, and to lead to the identification of many candidate genes involved in virulence.

Animals↗
PubMed2004

The complete genome and proteome of Mycoplasma mobile.

Although often considered "minimal" organisms, mycoplasmas show a wide range of diversity with respect to host environment, phenotypic traits, and pathogenicity. Here we report the complete genomic sequence and proteogenomic map for the piscine mycoplasma Mycoplasma mobile, noted for its robust gliding motility. For the first time, proteomic data are used in the primary annotation of a new genome, providing validation of expression for many of the predicted proteins. Several novel features were discovered including a long repeating unit of DNA of approximately 2435 bp present in five complete copies that are shown to code for nearly identical yet uniquely expressed proteins. M. mobile has among the lowest DNA GC contents (24.9%) and most reduced set of tRNAs of any organism yet reported (28). Numerous instances of tandem duplication as well as lateral gene transfer are evident in the genome. The multiple available complete genome sequences for other motile and immotile mycoplasmas enabled us to use comparative genomic and phylogenetic methods to suggest several candidate genes that might be involved in motility. The results of these analyses leave open the possibility that gliding motility might have arisen independently more than once in the mycoplasma lineage.

Amino Acid Sequence↗
PubMed2026

Mechanism-Driven Diagnostic Development: A Specimen-Aware Framework Illustrated by Colorectal Cancer and Solid Tumours.

Translational oncology has moved rapidly from histopathology and single-analyte biomarkers toward multi-dimensional molecular profiling. Yet many clinically deployed tests still use reductionist biomarker strategies that under-represent cancer complexity. This review examines whether a mechanistic, multi-layered, and specimen-aware approach can improve cancer detection, classification, prognosis, minimal residual disease (MRD) assessment, and therapeutic selection. Evidence across solid tumours shows that genomic alterations alone incompletely explain tumour state, metastatic behaviour, immune evasion, or therapeutic vulnerability. Integrated genome and transcriptome analyses, proteogenomics, single-cell atlases, fragmentomic, methylation based cell-free DNA assays, metabolomics and microbiome assessments reveal clinically relevant biology that single modality tests cannot determine. Minimally invasive collected specimens can extend access to screening, diagnosis and longitudinal monitoring, but the choice of specimen should be matched to disease biology and analytes that represent mechanisms of oncogenesis. However, translation remains constrained by pre-analytical variability, contamination, differences in tumour shedding behaviour, clonal haematopoiesis, translation of generated models, incomplete external validation and uncertain downstream clinical utility for emerging platforms. This review provides a commentary on the future of cancer diagnostics, the considerations and barriers to clinical translation, the relationship between utility and dimensionality of biomarkers assessed and the emerging rationale towards mechanistically grounded integrated models.

biomarkers↗
PubMed2001

[Biological principles of chemotherapy: cellular response as a result of its evolutionary memory].

The cell reaction to chemotherapy is based on certain general biological principles. The genetic structure of cell populations, their actual reaction to chemotherapy and the prognosis of their development are not accidental. They are determined by previous exposures to chemotherapeutic agents and thus their evolutional experience on Darwinian principles. Chemotherapy occurs always in a defined environment of the host with his active participation. A genetic variability in the host reaction to chemotherapeutic interference also exists and which in the long-term perspective is also of evolutional nature. The complex of evaluation of therapeutic procedures thus involves evaluation and influencing of the host and his variability (immunotherapy). Understanding of molecular mechanisms of the pathogenesis of disease, definition of specific genes, their expression, the role of their products and definition of possible targets of chemotherapy is at present a realistic goal of application of genomics and proteogenomics in medicine. The new philosophy in medicine is thus based on respecting individuality in diagnosis, treatment and prognosis. We only can wish that the trend of respecting individuality will be applied in the future, and not only in medicine.

Animals↗
PubMed2026

Pan-Cancer Quantification of Driver Alteration Transmission Across Molecular Layers Reveals Limited Propagation to Protein Abundance.

Precision oncology relies primarily on DNA-level alterations for therapeutic decisions, but the extent to which driver mutations propagate to protein abundance has not been systematically evaluated. Here, I developed a regression-based transmission score (TS_R 2) to quantify driver alteration signal propagation across DNA, mRNA, and protein layers. Applying this framework to matched genomic, transcriptomic, proteomic, and phosphoproteomic data from 754 Clinical Proteomic Tumor Analysis Consortium (CPTAC) tumors across seven cancer types, I analyzed 86 driver gene-cancer type pairs, of which 83 were evaluable for the full two-layer transmission score. I employed covariate-adjusted regression for each molecular transition, assessing significance via permutation testing (n = 1000). Mixed-effects modeling then partitioned gene-intrinsic from cancer-type-dependent effects. Only 5 of 83 evaluable pairs (6%) demonstrated high transmission (TS_R 2 > 0.05), with receptor tyrosine kinases (EGFR, FGFR2) exemplifying this class. The primary bottleneck occurred at the mutation-mRNA transition, not mRNA-protein translation. Gene identity accounted for 49% of transmission efficiency variance, nearly double the contribution of cancer type (29%). Copy number alterations transmitted signals 13.8-fold more efficiently than point mutations, and truncating mutations showed higher transmission than missense variants (Wilcoxon p = 0.005). Microsatellite instability attenuated mRNA-protein transmission in UCEC and COAD. These findings demonstrate that many driver alterations show limited propagation to protein abundance. This challenges DNA-only interpretations in precision oncology and provides a framework for integrated functional driver prioritization.

Humans↗
PubMed2025

Application of Proteomic Methods in Oomycete Biology.

The biochemical makeup of any organism provides insight into key factors regarding its biological functions. These factors can be explored using proteomics, which allows us to obtain a snapshot of the protein content and abundance in an organism, cell type or sub-cellular compartment. Here, we describe proteomic methodologies that can be used to dissect the biochemical mechanism of phytopathogenicity in oomycetes. These methodologies include protein extraction, purification, subsequent processing, mass spectrometry analysis, and qualitative and quantitative data processing of oomycete proteomes for comparative studies. Additionally, the use of mass spectra to assist in gene validation and modelling in unfinished oomycete genomes is also described.

Oomycetes↗
PubMed2026

Beyond Canonical Neoantigens: Emerging Technologies for Identification of Noncanonical Antigens and Implications for Personalized Cancer Vaccines.

Over the past decade, advances in sequencing technologies and computational pipelines enabled the development of personalized cancer vaccines (PCVs). Current PCV strategies primarily target cancer neoantigens generated by non-synonymous DNA mutations, which can result in altered amino acid sequences capable of eliciting tumor-specific immune responses. More recently, a distinct class of tumor-specific antigens (TSA), termed noncanonical or cryptic antigens, has emerged as an additional source of immunogenic targets. Unlike canonical neoantigens, noncanonical antigens typically cannot be identified by tumor/normal whole-exome sequencing, as they do not arise from classical DNA mutations. Instead, they are often associated with less well recognized and/or aberrant processes in the pathways from DNA to human leukocyte antigen (HLA)-presented peptides. Examples include transposable elements, circular RNA, translation of alternative open reading frames and/or long non-coding RNA, among others. Emerging evidence suggests that noncanonical antigens represent a substantial portion of the tumor-specific immunopeptidome and, similar to canonical neoantigens, are absent during thymic selection and can evade central tolerance and elicit T cell responses. Technological advances have increasingly facilitated the identification of noncanonical antigens. Long-read RNA sequencing reveals noncanonical transcripts by improving transcriptome assembly, while ribosome profiling provides genome-wide maps of actively translated regions, facilitating the discovery of peptides from aberrant translation events. Specialized molecular approaches enable enrichment and sequencing of circular RNAs, and immunopeptidomics using mass spectrometry allows for direct characterization of HLA-presented peptides. Together, these technological advances have led to an increasing interest in prioritizing and targeting noncanonical antigens in the next generation of PCVs. This review provides an overview of the diverse origins of TSAs beyond classical neoantigens and discusses emerging approaches that may enable the integration of these antigens in future clinical trials.

circular RNA↗